RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints

We propose a Convolutional Neural Network (CNN)-based model "RotationNet," which takes multi-view images of an object as input and jointly estimates its pose and object category. Unlike previous approaches that use known viewpoint labels for training, our method treats the viewpoint labels as latent variables, which are learned in an unsupervised manner during the training using an unaligned object dataset. RotationNet is designed to use only a partial set of multi-view images for inference, and this property makes it useful in practical scenarios where only partial views are available. Moreover, our pose alignment strategy enables one to obtain view-specific feature representations shared across classes, which is important to maintain high accuracy in both object categorization and pose estimation. Effectiveness of RotationNet is demonstrated by its superior performance to the state-of-the-art methods of 3D object classification on 10- and 40-class ModelNet datasets. We also show that RotationNet, even trained without known poses, achieves the state-of-the-art performance on an object pose estimation dataset. The code is available on https://github.com/kanezaki/rotationnetComment: 24 pages, 23 figures. Accepted to CVPR 201

Assignment of teachers engaged solely to the education and recommendation of early-graduation program

Education in university is important for lasting democracy. In order to grow the education widely, the financial aid from government is indispensible. It has been announced that this aid will be given when the revision is promoted to reinforce roles of the university. For this purpose, it is proposed that, teachers engaged solely to the education should be assigned in small-scaled universities. Moreover, early-graduation program is recommended in favor of encouraging diligent learning to undergraduate students

Effects of dielectric permittivities on skin heating due to millimeter wave exposure

<p>Abstract</p> <p>Background</p> <p>Because the possibility of millimeter wave (MMW) exposure has increased, public concern about the health issues due to electromagnetic radiation has also increased. While many studies have been conducted for MMW exposure, the effect of dielectric permittivities on skin heating in multilayer/heterogeneous human-body models have not been adequately investigated. This is partly due to the fact that a detailed investigation of skin heating in a multilayer model by computational methods is difficult since many parameters are involved. In the present study, therefore, theoretical analyses were conducted to investigate the relationship between dielectric permittivities and MMW-induced skin heating in a one-dimensional three-layer model (skin, fat, and muscle).</p> <p>Methods</p> <p>Approximate expressions were derived for the temperature elevation and temperature difference in the skin due to MMW exposure from analytical solutions for the temperature distribution. First, the power absorption distribution was approximated from the analytical solution for a one-layer model (skin only). Then, the analytical expression of the temperature in the three-layer model was simplified on the basis of the proposal in our previous study. By examining the approximate expressions, the dominant term influencing skin heating was clarified to identify the effects of the dielectric permittivities. Finally, the effects of dielectric permittivities were clarified by applying partial differentiation to the derived dominant term.</p> <p>Results</p> <p>Skin heating can be characterized by the parameters associated with the dielectric permittivities, independently of morphological and thermal parameters. With the derived expressions, it was first clarified that skin heating correlates with the total power absorbed in the skin rather than the specific absorption rate (SAR) at the skin surface or the incident power density. Using Debye-type expression we next investigated the effect of frequency dispersion on the complex relative permittivity of tissue. The parametric study on the total power absorbed in the skin showed that skin heating increases as the static permittivity and static conductivity decrease. In addition, the maximum temperature elevation on the body surface was approximately 1.6 times that of the minimum case. This difference is smaller than the difference caused by the thermal and morphological parameters reported in our previous study.</p> <p>Conclusion</p> <p>This paper analytically clarified the effects of dielectric permittivities on the thermally steady state temperature elevation and the temperature difference in the skin of a one-dimensional three-layer model due to MMW exposure.</p

Tactile Estimation of Extrinsic Contact Patch for Stable Placement

Precise perception of contact interactions is essential for the fine-grained manipulation skills for robots. In this paper, we present the design of feedback skills for robots that must learn to stack complex-shaped objects on top of each other. To design such a system, a robot should be able to reason about the stability of placement from very gentle contact interactions. Our results demonstrate that it is possible to infer the stability of object placement based on tactile readings during contact formation between the object and its environment. In particular, we estimate the contact patch between a grasped object and its environment using force and tactile observations to estimate the stability of the object during a contact formation. The contact patch could be used to estimate the stability of the object upon the release of the grasp. The proposed method is demonstrated on various pairs of objects that are used in a very popular board game.Comment: Under submissio